USING REGRESSION METHODS TO ESTIMATE STREAM PHOSPHORUS LOADS AT THE ILLINOIS RIVER, ARKANSAS
2003
The development of total maximum daily loads (TMDLs) requires evaluating existing constituent loads in streams.
Accurate estimates of constituent loads are needed to calibrate watershed and reservoir models for TMDL development. The
best approach to estimate constituent loads is high frequency sampling, particularly during storm events, and mass integration
of constituents passing a point in a stream. Most often, resources are limited and discrete water quality samples are collected
on fixed intervals and sometimes supplemented with directed sampling during storm events. When resources are limited, mass
integration is not an accurate means to determine constituent loads and other load estimation techniques such as regression
models are used. The objective of this work was to determine a minimum number of water–quality samples needed to provide
constituent concentration data adequate to estimate constituent loads at a large stream. Twenty sets of water quality samples
with and without supplemental storm samples were randomly selected at various fixed intervals from a database at the Illinois
River, northwest Arkansas. The random sets were used to estimate total phosphorus (TP) loads using regression models. The
regression–based annual TP loads were compared to the integrated annual TP load estimated using all the data. At a
minimum, monthly sampling plus supplemental storm samples (six samples per year) was needed to produce a root mean
square error of less than 15%. Water quality samples should be collected at least semi–monthly (every 15 days) in studies
less than two years if seasonal time factors are to be used in the regression models. Annual TP loads estimated from
independently collected discrete water quality samples further demonstrated the utility of using regression models to estimate
annual TP loads in this stream system.
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